Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, optimization of quantum circuits remains critical, especially for noisy intermediate-scale quantum (NISQ) devices with limited qubits and high error rates. Genetic algorithms (GAs) provide a promising approach for efficient quantum circuit synthesis by automating optimization tasks. This work examines the impact of various mutation strategies within a GA framework for quantum circuit synthesis. By analyzing how different mutations transform circuits, it identifies strategies that enhance efficiency and performance. Experiments utilized a fitness function emphasizing fidelity, while accounting for circuit depth and T-operations, to optimize circuits with four to six qubits. Our analysis revealed that, while the "swap, addition" strategy achieved the highest fidelity scores, it consistently increased circuit depth. In contrast, combining "swap, addition, delete" strategies offers a more balanced approach, delivering near-optimal results while also having the potential of reducing circuit depth.

We apply a hybrid evolutionary algorithm to minimize the depth of circuits in quantum computing. More specifically, we evaluate two different variants of the algorithm. In the first approach, we combine the evolutionary algorithm with an optimization subroutine to optimize the parameters of the rotation gates present in the quantum circuit. In the second, the algorithm solely relies on evolutionary operations (i.e., mutations and crossover). We approach the problem from two sides: (1) constructing circuits from the ground up by starting with random initializations and (2) initializing individuals with a target circuit in order to optimize it further according to the fitness function. We run experiments on random circuits with 4 and 6 qubits varying in circuit depth. Our results show that the proposed methods are able to significantly reduce the depth of circuits while still retaining a high fidelity to the target state.

While exploration in single-agent reinforcement learning has been studied extensively in recent years, consid-erably less work has focused on its counterpart in multi-agent reinforcement learning. To address this issue, this work proposes a peer-incentivized reward function inspired by previous research on intrinsic curiosity and influence-based rewards. The PIMAEX reward, short for Peer-Incentivized Multi-Agent Exploration, aims to improve exploration in the multi-agent setting by encouraging agents to exert influence over each other to increase the likelihood of encountering novel states. We evaluate the PIMAEX reward in conjunction with PIMAEX-Communication, a multi-agent training algorithm that employs a communication channel for agents to influence one another. The evaluation is conducted in the Consume/Explore environment, a partially observable environment with deceptive rewards, specifically designed to challenge the exploration vs. exploitation dilemma and the credit-assignm ent problem. The results empirically demonstrate that agents using the PI-MAEX reward with PIMAEX-Communication outperform those that do not.

This book contains the proceedings of the 17th International Conference on Agents and Artificial Intelligence. This year, ICAART is held in Porto, Portugal, on February 23-25, 2025. As usual it is sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). ICAART 2025 was also organized in cooperation with other members of our AI family. We mention the ACM Special Interest Group on Artificial Intelligence, the Association for the Advancement of Artificial Intelligence, the Associação Portuguesa de Reconhecimento de Padrões, the Portuguese Association for Artificial Intelligence, the IberoAmerican Society of Artificial Intelligence and the European Society for Fuzzy Logic and Technology. The purpose of the International Conference on Agents and Artificial Intelligence is to bring together researchers, engineers and practitioners interested in the theory and applications in the areas of Agents and Artificial Intelligence, covering both applications and current (advanced) research work. On one side it focuses on Agents, Multi-Agent Systems and Software Platforms, and also Distributed Problem Solving. On the other side it focuses on Artificial Intelligence, Knowledge Representation, Planning, Learning, Scheduling, Perception. Applications are in both areas. They are using Natural Language Processing (NLP), Large Language Models (LLMs), Legal Technologies and Quantum Computing. In the last four years the research emphasis has shifted towards Explainable AI and Interpretable AI with a focus on trustworthiness, fairness, privacy, safety, security and ethical issues. A substantial amount of research work is ongoing in these knowledge areas, in an attempt to discover appropriate theories and paradigms for use in real-world applications. ICAART 2025 received 472 paper submissions from 53 countries of which 23.09% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least 2 but usually 3 or more members of the International Program Committee, which is composed of established researchers and domain experts. The high quality of the ICAART 2025 program is enhanced by the keynote lecture delivered by distinguished speakers who are renowned experts in their fields: Inge Bryan (Chair of the Dutch Institute for Vulnerability Disclosure, Netherlands), Pavan Duggal (Advocate, Supreme Court of India, Chairman, International Commission on Cyber Security Law India, and Chief Executive, Artificial Intelligence Law Hub, India) and Paul Nemitz (Principal Adviser European Commission, Belgium). The conference is complemented by one workshop, two special sessions and one tutorial. They are: a Workshop on Quantum Artificial Intelligence and Optimization, chaired by Michael Kölle, a Special Session on Interpretable Artificial Intelligence Through Glass-Box Models, chaired by Mattias Wahde and a Special Session on Emotions and Affective Agents, chaired by Joaquin Taverner and Emilio Vivancos. Furthermore, a Tutorial on Self-Governing Systems will be given by Jeremy Pitt and Asimina Mertzani. All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index. As recognition for the best contributions, several awards based on the combined marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as assessed by session chairs at the conference venue, are conferred at the closing session of the conference. Authors of selected papers will be invited to submit extended versions for inclusion in a forthcoming book of ICAART Selected Papers to be published by Springer, as part of the LNAI Series. Some papers will also be selected for publication of extended and revised versions in the special issue of the Springer Nature Computer Science Journal. The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are herewith recorded. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly competent team. We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of ICAART, details of which are available at https://icaart.scitevents.org.

To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format. Qandle is an open-source project hosted on GitHub and PyPI.

This study presents an approach for prosody-driven segmentation of speech data. The model is based solely on F contours and RMS envelopes. Phoneme or word information from a speech recognizer is unneccesary. Using data from German broadcast news, we show how this prosodic information can be exploited to retrieve structural information of the spoken text. The suitability of the CART-like algorithm for utterance boundary prediction has been evaluated on 7 five-minutes-news-reports, using 28 reports as training material for the classification tree. Sentence boundaries were predicted with a precision of 93%, at a recall of 88%.